Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis

Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis

Deep Venous Thrombosis (DVT) is a manifestation of a Thromboembolic Disease (ET). When in a DVT the venous thrombus detaches and travel through the bloodstream can cause a Pulmonary Embolism Thrombus (PET). The existence of Deep Venous Thrombosis (DVT) in the lower extremities has been described as...

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Autores principales: Fong-Mata, Maria Berenice, Inzunza-González, Everardo, García-Guerrero, Enrique Efrén, Mejía Medina, David Abdel, Morales Contreras, Oscar Adrián, Gómez-Roa, Antonio
פורמט: Online
שפה:spa
יצא לאור: Universidad Autónoma de Baja California 2020
גישה מקוונת:https://recit.uabc.mx/index.php/revista/article/view/10
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id recit-article-10
record_format ojs
institution RECIT
collection OJS
language spa
format Online
author Fong-Mata, Maria Berenice
Inzunza-González, Everardo
García-Guerrero, Enrique Efrén
Mejía Medina, David Abdel
Morales Contreras, Oscar Adrián
Gómez-Roa, Antonio
spellingShingle Fong-Mata, Maria Berenice
Inzunza-González, Everardo
García-Guerrero, Enrique Efrén
Mejía Medina, David Abdel
Morales Contreras, Oscar Adrián
Gómez-Roa, Antonio
Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
author_facet Fong-Mata, Maria Berenice
Inzunza-González, Everardo
García-Guerrero, Enrique Efrén
Mejía Medina, David Abdel
Morales Contreras, Oscar Adrián
Gómez-Roa, Antonio
author_sort Fong-Mata, Maria Berenice
title Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
title_short Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
title_full Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
title_fullStr Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
title_full_unstemmed Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
title_sort deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis
description Deep Venous Thrombosis (DVT) is a manifestation of a Thromboembolic Disease (ET). When in a DVT the venous thrombus detaches and travel through the bloodstream can cause a Pulmonary Embolism Thrombus (PET). The existence of Deep Venous Thrombosis (DVT) in the lower extremities has been described as one of the main risk factors for the development of PET. It is considered that up to 90% of pulmonary emboli come from venous thrombi of the lower extremities. The most commonly used techniques for the detection of DVT are clinical probability models, D-dimer and non-invasive imaging tests, such as ultrasound for DVT and computed angiotomography (CT) for pulmonary embolism. However, due to the non-specificity of the symptoms of DVT, the threshold for ordering an ultrasound is low, in addition to being a complicated process that requires the participation of a specialist doctor for its interpretation. In recent decades, machine learning has emerged as support in decision-making for the diagnosis of various diseases, some of the most used technologies in the field of medicine include Support Vector Machine (SVM), Decision Trees and Neural Networks Artificial (RNA). This article reviews the existing technologies for the detection of DVT as well as the main machine learning algorithms commonly used in biomedical applications; The design of a computerized system that uses machine learning techniques as a support tool for the timely detection of a possible DVT is proposed.
publisher Universidad Autónoma de Baja California
publishDate 2020
url https://recit.uabc.mx/index.php/revista/article/view/10
_version_ 1792095338506485760
spelling recit-article-102022-10-20T22:46:25Z Deep vein thrombosis in lower extremities: review of current diagnostic techniques and their symbiosis with machine learning for timely diagnosis Trombosis venosa profunda en extremidades inferiores: revisión de las técnicas de diagnóstico actuales y su simbiosis con el aprendizaje automático para un diagnóstico oportuno Fong-Mata, Maria Berenice Inzunza-González, Everardo García-Guerrero, Enrique Efrén Mejía Medina, David Abdel Morales Contreras, Oscar Adrián Gómez-Roa, Antonio Diagnosis Artificial neural networks Deep venous thrombosis. Diagnóstico Redes neuronales artificiales Trombosis venosa profunda. Deep Venous Thrombosis (DVT) is a manifestation of a Thromboembolic Disease (ET). When in a DVT the venous thrombus detaches and travel through the bloodstream can cause a Pulmonary Embolism Thrombus (PET). The existence of Deep Venous Thrombosis (DVT) in the lower extremities has been described as one of the main risk factors for the development of PET. It is considered that up to 90% of pulmonary emboli come from venous thrombi of the lower extremities. The most commonly used techniques for the detection of DVT are clinical probability models, D-dimer and non-invasive imaging tests, such as ultrasound for DVT and computed angiotomography (CT) for pulmonary embolism. However, due to the non-specificity of the symptoms of DVT, the threshold for ordering an ultrasound is low, in addition to being a complicated process that requires the participation of a specialist doctor for its interpretation. In recent decades, machine learning has emerged as support in decision-making for the diagnosis of various diseases, some of the most used technologies in the field of medicine include Support Vector Machine (SVM), Decision Trees and Neural Networks Artificial (RNA). This article reviews the existing technologies for the detection of DVT as well as the main machine learning algorithms commonly used in biomedical applications; The design of a computerized system that uses machine learning techniques as a support tool for the timely detection of a possible DVT is proposed. La Trombosis Venosa Profunda (TVP) es una manifestación de una Enfermedad Tromboembólica (ET). Cuando en una TVP los trombos venosos se desprenden y viajan a través del torrente sanguíneo pueden ocasionar una Trombo Embolia Pulmonar (TEP). La existencia de Trombosis Venosa Profunda (TVP) en las extremidades inferiores se ha descrito como uno de los principales factores de riesgo para el desarrollo de la TEP. Se considera que hasta el 90% de los émbolos pulmonares proceden de trombos venosos de las extremidades inferiores. Las técnicas más utilizadas para la detección de TVP son los modelos de probabilidad clínica, el dímero D y las pruebas de imagen no invasivas, como la ecografía para la TVP y la angiotomografía computadorizada (TC) para el embolismo pulmonar. Sin embargo, debido a la inespecificidad de los síntomas de la TVP, el umbral para ordenar una ecografía es bajo, además de ser un proceso complicado que requiere la participación de un médico especialista para su interpretación. En las últimas décadas el aprendizaje automático ha surgido como apoyo en la toma de decisiones para el diagnóstico de diversas enfermedades, algunas de las tecnologías más utilizadas en el campo de la medicina incluyen Support Vector Machine (SVM), Árboles de decisión y las Redes Neuronales Artificiales (RNA). En el presente artículo se hace una revisión de las tecnologías existentes para la detección de la TVP así como de los principales algoritmos de aprendizaje automático comúnmente utilizados en aplicaciones biomédicas; se propone el diseño de un sistema computarizado que utilice técnicas de aprendizaje automático como herramienta de apoyo para la detección oportuna de un posible padecimiento de TVP. Universidad Autónoma de Baja California 2020-07-15 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/html application/xml https://recit.uabc.mx/index.php/revista/article/view/10 10.37636/recit.v312334 REVISTA DE CIENCIAS TECNOLÓGICAS; Vol. 3 No. 1 (2020): January-March; 23-34 REVISTA DE CIENCIAS TECNOLÓGICAS; Vol. 3 Núm. 1 (2020): Enero-Marzo; 23-34 2594-1925 spa https://recit.uabc.mx/index.php/revista/article/view/10/19 https://recit.uabc.mx/index.php/revista/article/view/10/44 https://recit.uabc.mx/index.php/revista/article/view/10/168 Copyright (c) 2020 Fong-Mata María Berenice, Inzunza-González Everardo, García-Guerrero Enrique Efrén, Mejía Medina David Abdel, Morales Contreras Oscar Adrián, Gómez-Roa Antonio http://creativecommons.org/licenses/by/4.0

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